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A semi-supervised privacy-preserving clustering algorithm for healthcare

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Abstract

With the proliferation of healthcare data, the cloud mining technology for E-health services and applications has become a hot research topic. While on the other hand, these rapidly evolving cloud mining technologies and their deployment in healthcare systems also pose potential threats to patient’s data privacy. In order to solve the privacy problem in the cloud mining technique, this paper proposes a semi-supervised privacy-preserving clustering algorithm. By employing a small amount of supervised information, the method first learns a Large Margin Nearest Cluster metric using convex optimization. Then according to the trained metric, the method imposes multiplicative perturbation on the original data, which can change the distribution shape of the original data and thus protect the privacy information as well as ensuring high data usability. The experimental results on the brain fiber dataset provided by the 2009 PBC demonstrated that the proposed method could not only protect data privacy towards secure attacks, but improve the clustering purity.

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Authors and Affiliations

  1. Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Meiyu Huang, Yiqiang Chen, Junfa Liu & Wen Ji

  2. University of Chinese Academy of Sciences, Beijing, China

    Meiyu Huang

  3. Department of Electrical Engineering, Princeton University, Princeton, NJ, USA

    Bo-Wei Chen

  4. Department of Multimedia, Sungkyul University, Sungkyul, South Korea

    Seungmin Rho

Authors
  1. Meiyu Huang
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  2. Yiqiang Chen
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  3. Bo-Wei Chen
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  4. Junfa Liu
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  5. Seungmin Rho
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  6. Wen Ji
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Corresponding author

Correspondence to Wen Ji.

Additional information

This work is supported in part by the National Natural Science Foundation of China (61173066, 61472399), and the Beijing Natural Science Foundation (4112056, 4122078, 4144085).

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Huang, M., Chen, Y., Chen, BW. et al. A semi-supervised privacy-preserving clustering algorithm for healthcare. Peer-to-Peer Netw. Appl. 9, 864–875 (2016). https://doi.org/10.1007/s12083-015-0356-9

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  • DOI: https://doi.org/10.1007/s12083-015-0356-9

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